1. Field of the Invention
The present invention relates generally to the field of virtual microscopy and pertains more specifically to data management for very large digital imaging files captured by a high resolution linear-array-based microscope slide scanner.
2. Related Art
Conventional scanners typically digitize a region of a physical specimen at a desired resolution. As the desired resolution increases, the scanning process becomes more technically challenging. Similarly, the scanning process becomes more challenging as the region of interest increases or as the available scanning time decreases. Furthermore, the efficiency with which the digitized data can be viewed on a monitor is often critical to the overall utility of conventional scanning applications.
Recent technical advances in conventional sensors, computers, storage capacity, and image management have made it possible to digitize an entire microscope slide at diagnostic resolution, which is particularly desirable. Diagnostic resolution is the resolution required for a trained technician or clinician to make a diagnosis directly from a computer monitor, rather than making a diagnosis by looking through the eyepieces of a conventional microscope. Diagnostic resolution varies by sample type, for example, the diagnostic resolution required for a skin biopsy specimen is typically lower (i.e., diagnosis requires a lower resolution) than the diagnostic resolution required for other types of biopsy specimens.
Although now technically possible, digitizing an entire microscope slide at a diagnostic resolution remains a formidable challenge. Any practical solution must capture immense amounts of high quality imagery data in a relatively short amount of time. FIG. 1 is a graph diagram plotting the limiting resolution in micrometers (“μm”) of an optical system with realistic condenser settings versus the numerical aperture (“NA”) for the optical system's microscope objective lens. The limiting resolution is defined as the smallest distance that can be resolved by the optical system. For example, in an optical system that is designed and manufactured appropriately, the limiting resolution would be the minimum spatial dimension that can be observed by the human eye.
As shown in the graph, the limiting resolution for an objective lens with a 0.3 NA is approximately 1.5 μm. Moreover, the limiting resolution for an objective lens with a 0.4 NA improves to about 1 μm while the limiting resolution for an objective lens with a 0.8 NA improves to an even better 0.5 μm. At this juncture, it is important to note that the limiting resolution is independent of magnification and depends solely on the numerical aperture of the objective lens.
Conventional systems that digitize a microscope specimen without losing any details available to the human eye require the dimension of a detector element to be no larger than one half the corresponding limiting resolution distance. This 2-pixel requirement is based on the well-known Nyquist sampling theorem. It should be clear that for a 2-dimensional imaging system, the 2-pixel requirement translates into an array of 2 pixels by 2 pixels. Stated differently, if the limiting resolution is 1 μm, then it is necessary to digitize the specimen at 0.5 μm per pixel (or better) to capture all of the information that is available to the human eye through the objective lens.
FIG. 2 is a graph diagram plotting the scanning resolution in pixels per inch (“ppi”) versus the numerical aperture of an objective lens. As shown in the graph, an objective lens with a 0.3 NA requires a scanning resolution of at least 38,000 ppi. This resolution is required to capture all of the details provided by the 0.03 NA objective lens and viewable by the human eye. Similarly, an objective lens with a 0.4 NA requires a scanning resolution of at least 50,000 ppi while an objective lens with a 0.8 NA requires a scanning resolution of at least 100,000 ppi.
FIG. 3 is a graph diagram plotting the scanning resolution in pixels per inch versus the resulting uncompressed file size in megabytes (“MB”) for a one square millimeter (“mm”) region. The graph pertains to regions captured as 24-bit pixels (3 color channels, 8-bits per channel). As illustrated, a 1 mm2 region at 38,000 ppi is approximately 8 MB (as captured by an objective lens with a 0.03 NA according to FIG. 2). Similarly, a higher scanning resolution of 50,000 ppi for the same 1 mm2 region would result in a file size of 11 MB while a scanning resolution of 100,000 ppi would result in a file size of approximately 47 MB. As can be seen, the size of the image file increases dramatically as the required scanning resolution, expressed in pixels per inch, increases in relation to the increasing numerical aperture of the objective lens. Thus, as the scanning resolution increases, the image file size increases significantly.
Accordingly, digitizing an entire microscope slide at a diagnostic resolution results in extremely large data files. For example, a typical 15 mm×15 mm slide region at a scanning resolution of 50,000 ppi (i.e., 0.4 NA) would result in a file size of approximately 2.5 gigabytes (“GB”). At a scanning resolution of 100,000 ppi, the resulting file size quadruples to approximately 10 GB for the same 225 square millimeter area of a slide.
There are two basic methods that have been developed for scanning entire microscope slides: (i) conventional image tiling, and (ii) a novel line-scanning method and system developed by Aperio Technologies, Inc. This latter method utilizes a linear-array detector in conjunction with specialized optics, as described in U.S. patent application Ser. No. 09/563,437, entitled “Fully Automatic Rapid Microscope Slide Scanner,” which is currently being marketed under the name ScanScope®.
Conventional image tiling is a well-known technique. Image tiling involves the capture of multiple small, statically sized regions of a microscope slide using a traditional fixed-area Charge-Coupled-Device (“CCD”) camera, with each capture tile being stored as a separate individual image file. Subsequently, the various image tiles that comprise a specimen are digitally “stitched” together (i.e., alignment) to create a large contiguous digital image of the entire slide.
The number of individual image tiles required to scan a given area of a slide is proportional to the number of pixels that comprise each image tile. A typical video-format color camera has 768×494 pixels, which translates into 1.1 MB of imagery data per image tile. Recalling that a 1 mm2 region of a slide corresponds to 11 MB of imagery data, it follows that approximately 10 non-overlapping image tiles must be captured to digitize one square millimeter of a slide at a scanning resolution of 50,000 ppi. At 100,000 ppi the required number of tiles increases four-fold to 40 image tiles per square millimeter.
It follows that for a typical 15 mm×15 mm slide region, at a scanning resolution of 50,000 ppi, a minimum of 2,250 individual image tiles must be captured. At a scanning resolution of 100,000 ppi, a minimum of 9,000 individual image tiles must be captured. Importantly, each image tile would have a file size of approximately 1.1 MB. In practice, an even larger number of tiles must be captured to provide sufficient overlap between adjacent tiles to facilitate the “stitching” together or alignment of adjacent image tiles.
Conventional image tiling systems generally take hours to capture and align the thousands of tiles required to digitize an entire microscope slide. Image capture times are significantly increased by the need to wait for the CCD camera to stabilize after being repositioned and before acquiring an image tile. This wait time is necessary to ensure that the captured image does not blur. Practical limitations in data processing speeds also make the alignment of large numbers of image tiles extremely slow. In practice, conventional image tiling systems are not able to align large numbers of tiles without creating “stitch lines” and other image artifacts that create computer imaging challenges.
An alternative to image tiling is the afore-mentioned line-scanning method. Rather than using a fixed-area camera to capture thousands of individual image tiles, the line-scanning method employs a linear-array detector in conjunction with a microscope objective lens and other optics to capture a small number of contiguous overlapping image stripes. Unlike the stop-and-go nature of conventional image tiling, the microscope slide moves continuously and at a constant velocity during acquisition of an image stripe. One of the many fundamental advantages of line-scanning over conventional image tiling is that the capture and alignment of a small number of image stripes is significantly more efficient than the capture and alignment of thousands of separately captured image tiles.
For example, a typical 15 mm×15 mm slide region at 50,000 ppi would require 15 image stripes, each with a width of 2,000 pixels, to digitally capture the region. Here, each image stripe would have a file size of approximately 170 MB. At 100,000 ppi, the same region would require 30 image stripes with each stripe comprising approximately 680 MB. The capture of 15 or 30 image stripes for a 15 mm×15 mm area is dramatically more efficient than the capture of 2,250 or 9,000 image tiles at 50,000 ppi or 100,000 ppi respectively. Furthermore, the continuous scanning nature of line-scanning makes it possible to create seamless virtual slides of a region in minutes.
In addition to rapid data capture, line scanning benefits from several advantages that ensure consistently superior imagery data. First, it is possible to adjust the focus of the objective lens from one scan line to the next, in contrast to image tiling systems that are inherently limited to a single focal plane for an entire image tile. Second, because the sensor in a line scanning system is one-dimensional, there are no optical aberrations along the scanning axis. In an image tiling system, the optical aberrations are circularly symmetric about the center of the image tile. Third, the linear detector has a one-hundred percent (100%) fill factor, providing full pixel resolution (8 bits per color channel), unlike color CCD cameras that lose spatial resolution because color values from non-adjacent pixels are interpolated (e.g., using a Bayer Mask).
To handle the immense amounts of data produced by conventional image tiling systems, data management tools have been developed to manage the thousands of relatively small (˜1MB) image tiles typically generated by such systems. These data management utilities, however, are not suitable for managing a small number of relatively large (˜200 MB) image stripes captured by the line-scanning image striping system.
Therefore, introduction of the superior image striping system and method for digitizing microscope slides has created a need in the industry for a data management system that meets the unique needs imposed by the new technology.